Sign Language Recognition System using ESP32-CAM and Cloud Integration

A real-time sign-to-text conversion system built with ESP32-CAM, CNN-based image recognition, and Supabase cloud integration to assist Deaf individuals with accessible communication.

This project focuses on accessible communication for Deaf individuals through real-time sign-to-text conversion. Using an ESP32-CAM, images of hand gestures are captured and processed through a lightweight Convolutional Neural Network (CNN). The results are integrated with Supabase cloud functions for real-time inference and stored for monitoring. Outputs are displayed on an LCD, with scope for a mobile app to improve accessibility. The system is designed to be portable, affordable, and scalable, aligning with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure).